Sandhu, J. K., Kumar, M., & Singh, A. (2026). Hybrid CNN-BLSTM support vector machine architecture for E2E speech recognition. International Journal of Speech Technology, 29(1), 7.

https://doi.org/10.1007/s10772-025-10238-5

Rani, V., & Kumar, M. (2025). Transfer learning for human gait recognition using VGG19: CASIA-A dataset. Multimedia Tools and Applications, 84(22), 25981-25995.

https://link.springer.com/article/10.1007/s11042-024-20132-y

Sukhija, R., Kumar, M., & Jindal, M. K. (2025). Document forgery detection: a comprehensive review. International Journal of Data Science and Analytics, 1-23.

https://link.springer.com/article/10.1007/s41060-025-00723-0

Rani, M., & Kumar, M. (2025). MobileNet for human activity recognition in smart surveillance using transfer learning. Neural Computing and Applications, 37(5), 3907-3924.

https://link.springer.com/article/10.1007/s00521-024-10882-z

Tuteja, S., Tonk, R., & Kumar, M. (2025). Intelligent transportation systems: filters and performance evaluation in image data decontamination. Multimedia Tools and Applications, 1-13.

https://link.springer.com/article/10.1007/s11042-025-21012-9

Rani, M., & Kumar, M. (2025). Efficient Human Activity Recognition Using a Hybrid MobileNetV2-CNN Model. National Academy Science Letters, 1-8.

https://link.springer.com/article/10.1007/s40009-025-01720-4

Singla, C., Maini, R., & Kumar, M. (2025). Gender classification based on handwriting using Gurmukhi characters and hybrid feature extraction techniques. Multimedia Tools and Applications, 84(19), 20823-20841.

https://link.springer.com/article/10.1007/s11042-024-19873-7

Sethi, M., Kumar, M., & Jindal, M. K. (2025). Forensic handwriting analysis: a hybrid classification framework for writer identification in Devanagari script. Multimedia Tools and Applications, 1-12.

https://link.springer.com/article/10.1007/s11042-024-20574-4

Dargan, S., Kumar, M., Mittal, A., & Kumar, K. (2024). Handwriting-based gender classification using machine learning techniques. Multimedia Tools and Applications, 83(7), 19871–19895.

https://link.springer.com/article/10.1007/s11042-023-16354-1

Farooq, U., Khurana, S. S., Singh, P., & Kumar, M. (2024). An Empirical Study on Detection of Android Adware Using Machine Learning Techniques. Multimedia Tools and Applications, 83(13), 38753–38792.

https://link.springer.com/article/10.1007/s11042-023-16920-7

Kaur, M., & Kumar, M. (2024). Facial emotion recognition: A comprehensive review. Expert Systems, 41(10), e13670.

https://doi.org/10.1111/exsy.13670

Dargan, S., & Kumar, M. (2024). Gender classification system based on the behavioral biometric modality: application of handwritten text. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(3), 1–21.

https://dl.acm.org/doi/10.1145/3626236

Bhardwaj, V., Gera, T., Thakur, D., & Singh, A. (2024). Enhancing automatic speech recognition for punjabi dialects: An experimental analysis of incorporating prosodic features and acoustic variability mitigation. SN Computer Science, 5(6), 747.

https://doi.org/10.1007/s42979-024-03111-w

Singh, S., Garg, N. K., & Kumar, M. (2024). VGG16: Offline handwritten devanagari word recognition using transfer learning. Multimedia Tools and Applications, 83(29), 72561–72594.

https://link.springer.com/article/10.1007/s11042-024-18394-7

Nabi, S. T., Kumar, M., & Singh, P. (2024). A convolution deep architecture for gender classification of urdu handwritten characters. Multimedia Tools and Applications, 83(29), 72179–72194.

https://link.springer.com/article/10.1007/s11042-024-18415-5

Rani, V., Kumar, M., Gupta, A., Sachdeva, M., Mittal, A., & Kumar, K. (2024). Self-supervised learning for medical image analysis: a comprehensive review. Evolving Systems, 15(4), 1607–1633.

https://link.springer.com/article/10.1007/s12530-024-09581-w

Shaheed, K., Abbas, Q., & Kumar, M. (2024). Automatic diagnosis of CoV-19 in CXR images using haar-like feature and XgBoost classifier. Multimedia Tools and Applications, 83(26), 67723–67745.

https://link.springer.com/article/10.1007/s11042-024-18330-9

Sidhu, S., Khurana, S. S., Kumar, M., Singh, P., & Bamber, S. S. (2024). Sentiment analysis of Hindi language text: a critical review. Multimedia Tools and Applications, 83(17), 51367–51396.

https://link.springer.com/article/10.1007/s11042-023-17537-6

Kaur, H., Rani, V., & Kumar, M. (2024). Human activity recognition: A comprehensive review. Expert Systems, 41(11), e13680.

https://doi.org/10.1111/exsy.13680

Farooq, U., Singh, P., Khurana, S. S., & Kumar, M. (2024). Detection of content-based cybercrime in Roman Kashmiri using ensemble learning. Multimedia Tools and Applications, 83(11), 33071–33105.

https://link.springer.com/article/10.1007/s11042-023-16678-y

Nabi, S. T., Kumar, M., & Singh, P. (2024). Correction to: A convolution deep architecture for gender classification of Urdu handwritten characters. Multimedia Tools and Applications, 83(29), 72195–72195.

https://link.springer.com/article/10.1007/s11042-024-18415-5

Singla, C., Maini, R., & Kumar, M. (2024). Age, gender and handedness prediction using handwritten text: A comprehensive survey. Engineering Applications of Artificial Intelligence, 128, 107432.

https://doi.org/10.1016/j.engappai.2023.107432

Kaur, H., Rani, V., Kumar, M., Sachdeva, M., Mittal, A., & Kumar, K. (2024). Federated learning: a comprehensive review of recent advances and applications. Multimedia Tools and Applications, 83(18), 54165–54188.

https://link.springer.com/article/10.1007/s11042-023-17737-0

Dargan, S., Bansal, S., Kumar, M., Mittal, A., & Kumar, K. (2023). Augmented reality: A comprehensive review. Archives of Computational Methods in Engineering, 30(2), 1057–1080.

https://link.springer.com/article/10.1007/s11831-022-09831-7

Kaur, A., Kumar, M., & Jindal, M. K. (2023). Cattle identification system: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multimedia Tools and Applications, 82(18), 27391–27413.

https://link.springer.com/article/10.1007/s11042-023-14478-y

Walia, S., Kumar, K., & Kumar, M. (2023). Unveiling digital image forgeries using Markov based quaternions in frequency domain and fusion of machine learning algorithms. Multimedia Tools and Applications, 82(3), 4517–4532.

https://link.springer.com/article/10.1007/s11042-022-13610-8

Maqbool, J., Mann, T. S., Kaur, N., Gupta, A., Mittal, A., Aggarwal, P., & Saini, S. S. (2023). SegCon: A Novel Deep Neural Network for Segmentation of Conjunctiva Region. Advances in Data-driven Computing and Intelligent Systems, 719–730.

https://link.springer.com/chapter/10.1007/978-981-99-0981-0_55

Misgar, M. M., Mushtaq, F., Khurana, S. S., & Kumar, M. (2023). Recognition of offline handwritten Urdu characters using RNN and LSTM models. Multimedia Tools and Applications, 82(2), 2053–2076.

https://link.springer.com/article/10.1007/s11042-022-13320-1

Nabi, S. T., Singh, P., & Kumar, M. (2023). Gender Classification from Offline Handwriting Images in Urdu Script: LeNet-5 and Alex-Net. IEEE ICAPAI.

https://ieeexplore.ieee.org/abstract/document/10194140

Kaur, G., Singh, N., & Kumar, M. (2023). Image forgery techniques: a review. Artificial Intelligence Review, 56(2), 1577–1625.

https://link.springer.com/article/10.1007/s10462-022-10211-7

Singh, S., Garg, N. K., & Kumar, M. (2023). Feature extraction and classification techniques for handwritten Devanagari text recognition: a survey. Multimedia Tools and Applications, 82(1), 747–775.

https://link.springer.com/article/10.1007/s11042-022-13318-9

Nabi, S. T., Kumar, M., & Singh, P. (2023). DeepNet-WI: a deep-net model for offline Urdu writer identification. Evolving Systems, 1–11.

https://link.springer.com/article/10.1007/s12530-023-09504-1

Sethi, M., Kumar, M., & Jindal, M. K. (2023). Gender prediction system through behavioral biometric handwriting: a comprehensive review. Soft Computing, 27(10), 6307–6327.

https://link.springer.com/article/10.1007/s00500-023-07907-5

Kaur, H., & Kumar, M. (2023). Signature identification and verification techniques: state-of-the-art work. Journal of Ambient Intelligence and Humanized Computing, 14(2), 1027–1045.

https://link.springer.com/article/10.1007/s12652-021-03356-w

Kaur, H., Bansal, S., Kumar, M., Mittal, A., & Kumar, K. (2023). Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network. Multimedia Tools and Applications, 82(30), 46763–46788.

https://link.springer.com/article/10.1007/s11042-023-15527-2

Singh, S., Garg, N. K., & Kumar, M. (2023). On the performance analysis of various features and classifiers for handwritten devanagari word recognition. Neural Computing and Applications, 35(10), 7509–7527.

https://link.springer.com/article/10.1007/s00521-022-08045-z

Rani, V., & Kumar, M. (2023). Human gait recognition: A systematic review. Multimedia Tools and Applications, 82(24), 37003–37037.

https://link.springer.com/article/10.1007/S11042-023-15079-5

Bansal, M., Kumar, M., Sachdeva, M., & Mittal, A. (2023). Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3609–3620.

https://link.springer.com/article/10.1007/s12652-021-03488-z

Mohiuddin, S., Malakar, S., Kumar, M., & Sarkar, R. (2023). A comprehensive survey on state-of-the-art video forgery detection techniques. Multimedia Tools and Applications, 82(22), 33499–33539.

https://link.springer.com/article/10.1007/s11042-023-14870-8

Sitender, Bawa, S., Kumar, M., & Sangeeta. (2023). A comprehensive survey on machine translation for English, Hindi and Sanskrit languages. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3441–3474.

https://link.springer.com/article/10.1007/s12652-021-03479-0

Kumar, M., Jindal, M. K., & Kumar, M. (2023). An efficient technique for breaking of coloured Hindi CAPTCHA. Soft Computing, 27(16), 11661–11686.

https://link.springer.com/article/10.1007/s00500-023-07844-3

Singh, A., Kukreja, V., & Kumar, M. (2023). An empirical study to design an effective agile knowledge management framework. Multimedia Tools and Applications, 82(8), 12191–12209.

https://link.springer.com/article/10.1007/s11042-022-13871-3

Singh, N., Kumar, M., Singh, B., & Singh, J. (2023). DeepSpacy-NER: an efficient deep learning model for named entity recognition for Punjabi language. Evolving Systems, 14(4), 673–683.

https://link.springer.com/article/10.1007/s12530-022-09453-1

Dhalla, S., Maqbool, J., Mann, T. S., Gupta, A., Mittal, A., Aggarwal, P., & Saini, S. S. (2023). Semantic segmentation of palpebral conjunctiva using predefined deep neural architectures for anemia detection. Procedia Computer Science, 218, 328–337.

https://doi.org/10.1016/j.procs.2023.01.015

Kaur, R. P., Kumar, M., & Jindal, M. K. (2023). Performance evaluation of different features and classifiers for Gurumukhi newspaper text recognition. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10245–10261.

https://link.springer.com/article/10.1007/s12652-021-03687-8

Rani, V., Nabi, S. T., Kumar, M., Mittal, A., & Kumar, K. (2023). Self-supervised learning: A succinct review. Archives of Computational Methods in Engineering, 30(4), 2761–2775.

https://link.springer.com/article/10.1007/s11831-023-09884-2

Kaur, H., Kumar, M., Gupta, A., Sachdeva, M., Mittal, A., & Kumar, K. (2023). Bagging: An ensemble approach for recognition of handwritten place names in gurumukhi script. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(7), 1–25.

https://dl.acm.org/doi/10.1145/3593024

Kaur, A., Kumar, M., & Jindal, M. K. (2022). Cattle identification with muzzle pattern using computer vision technology: a critical review and prospective. Soft Computing, 26(10).

https://link.springer.com/article/10.1007/s00500-022-06935-x

Kaur, A., Kumar, M., & Jindal, M. K. (2022). Shi-Tomasi corner detector for cattle identification from muzzle print image pattern. Ecological Informatics, 68, 101549.

https://doi.org/10.1016/j.ecoinf.2021.101549

Singh, A., Kaur, N., Kukreja, V., Kadyan, V., & Kumar, M. (2022). Computational intelligence in processing of speech acoustics: a survey. Complex & Intelligent Systems, 8(3), 2623–2661.

https://doi.org/10.1007/s40747-022-00665-1

Kaur, R. P., Jindal, M. K., & Kumar, M. (2022). LineSeg: line segmentation of scanned newspaper documents. Pattern Analysis and Applications, 25(1), 189–208.

https://link.springer.com/article/10.1007/s10044-021-01031-6

Shaheed, K., Mao, A., Qureshi, I., Kumar, M., Hussain, S., Ullah, I., & Zhang, X. (2022). DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition. Expert Systems with Applications, 191, 116288.

https://www.sciencedirect.com/science/article/abs/pii/S0957417421015943

Kumar, M., Jindal, M. K., & Kumar, M. (2022). Design of innovative CAPTCHA for hindi language. Neural Computing and Applications, 34(6), 4957–4992.

https://link.springer.com/article/10.1007/s00521-021-06686-0

Nabi, S. T., Kumar, M., Singh, P., Aggarwal, N., & Kumar, K. (2022). A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions. Multimedia Systems, 28(3), 939–992.

https://link.springer.com/article/10.1007/s00530-021-00873-8

Kumar, M., Jindal, M. K., & Kumar, M. (2022). Distortion, rotation and scale invariant recognition of hollow Hindi characters. Sādhanā, 47(2), 92.

https://link.springer.com/article/10.1007/s12046-022-01847-w

Chaturvedi, V., Kaur, A. B., Varshney, V., Garg, A., Chhabra, G. S., & Kumar, M. (2022). Music mood and human emotion recognition based on physiological signals: a systematic review. Multimedia Systems, 28(1), 21–44.

https://link.springer.com/article/10.1007/s00530-021-00786-6

Jyotiyana, M., Kesswani, N., & Kumar, M. (2022). A deep learning approach for classification and diagnosis of Parkinson’s disease. Soft Computing, 26(18), 9155–9165.

https://link.springer.com/article/10.1007/s00500-022-07275-6

Shaheed, K., Mao, A., Qureshi, I., Abbas, Q., Kumar, M., & Zhang, X. (2022). Finger-vein presentation attack detection using depthwise separable convolution neural network. Expert Systems with Applications, 198, 116786.

https://doi.org/10.1016/j.eswa.2022.116786

Bathla, A. K., Bansal, S., & Kumar, M. (2022). OKC classifier: an efficient approach for classification of imbalanced dataset using hybrid methodology. Soft Computing, 26(21), 11497–11503.

https://link.springer.com/article/10.1007/s00500-022-07441-w

Kumar, M., Jindal, M. K., & Kumar, M. (2022). A systematic survey on CAPTCHA recognition: types, creation and breaking techniques. Archives of Computational Methods in Engineering, 29(2), 1107–1136.

https://link.springer.com/article/10.1007/s11831-021-09608-4

Dhiman, B., Kumar, Y., & Kumar, M. (2022). Fruit quality evaluation using machine learning techniques: review, motivation and future perspectives. Multimedia Tools and Applications, 81(12), 16255–16277.

https://link.springer.com/article/10.1007/s11042-022-12652-2

Koul, S., Kumar, M., Khurana, S. S., Mushtaq, F., & Kumar, K. (2022). An efficient approach for copy-move image forgery detection using convolution neural network. Multimedia Tools and Applications, 81(8), 11259–11277.

https://link.springer.com/article/10.1007/s11042-022-11974-5

Maity, S., Bhattacharyya, A., Singh, P. K., Kumar, M., & Sarkar, R. (2022). Last decade in vehicle detection and classification: a comprehensive survey. Archives of Computational Methods in Engineering, 29(7), 5259–5296.

https://link.springer.com/article/10.1007/s11831-022-09764-1

Rani, V., Kumar, M., Mittal, A., & Kumar, K. (2022). Artificial intelligence for cybersecurity: recent advancements, challenges and opportunities. Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities, 73–88.

https://link.springer.com/chapter/10.1007/978-3-030-96737-6_4

Shaheed, K., Mao, A., Qureshi, I., Kumar, M., Hussain, S., & Zhang, X. (2022). Recent advancements in finger vein recognition technology: methodology, challenges and opportunities. Information Fusion, 79, 84–109.

https://doi.org/10.1016/j.inffus.2021.10.004

Arora, N., Kumar, Y., Karkra, R., & Kumar, M. (2022). Automatic vehicle detection system in different environment conditions using fast R-CNN. Multimedia Tools and Applications, 81(13), 18715–18735.

https://link.springer.com/article/10.1007/s11042-022-12347-8

Rani, S., Kaur, M., & Kumar, M. (2022). Recommender system: prediction/diagnosis of breast cancer using hybrid machine learning algorithm. Multimedia Tools and Applications, 81(7), 9939–9948.

https://link.springer.com/article/10.1007/s11042-022-12144-3

Ahuja, U., Singh, S., Kumar, M., Kumar, K., & Sachdeva, M. (2022). COVID-19: Social distancing monitoring using faster-RCNN and YOLOv3 algorithms. Multimedia Tools and Applications, 82(5), 7553–7566.

https://doi.org/10.1007/s11042-022-13718-x

Jan, T. G., Khurana, S. S., & Kumar, M. (2022). Semi-supervised labeling: a proposed methodology for labeling the twitter datasets. Multimedia Tools and Applications, 81(6), 7669–7683.

https://link.springer.com/article/10.1007/s11042-022-12221-7

Arora, M., & Kumar, M. (2021). AutoFER: PCA and PSO based automatic facial emotion recognition. Multimedia Tools and Applications, 80(2), 3039–3049.

https://link.springer.com/article/10.1007/s11042-020-09726-4

Dargan, S., Kumar, M., & Tuteja, S. (2021). PCA-based gender classification system using hybridization of features and classification techniques. Soft Computing, 25(24), 15281–15295.

https://link.springer.com/article/10.1007/s00500-021-06118-0

Kumar, Y., Singh, N., Kumar, M., & Singh, A. (2021). AutoSSR: an efficient approach for automatic spontaneous speech recognition model for the Punjabi Language. Soft Computing, 25(2), 1617–1630.

https://doi.org/10.1007/s00500-020-05248-1

Rani, S., & Kumar, M. (2021). Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT. Multimedia Tools and Applications, 80(16), 24555–24571.

https://link.springer.com/article/10.1007/s11042-021-10823-1

Narang, S. R., Kumar, M., & Jindal, M. K. (2021). DeepNetDevanagari: a deep learning model for Devanagari ancient character recognition. Multimedia Tools and Applications, 80(13), 20671–20686.

https://link.springer.com/article/10.1007/s11042-021-10775-6

Singh, H., Sharma, R. K., Singh, V. P., & Kumar, M. (2021). Recognition of online handwritten Gurmukhi characters using recurrent neural network classifier. Soft Computing, 25(8), 6329–6338.

https://link.springer.com/article/10.1007/s00500-021-05620-9

Bansal, M., Kumar, M., & Kumar, M. (2021). 2D object recognition techniques: state-of-the-art work. Archives of Computational Methods in Engineering, 28(3), 1147–1161.

https://doi.org/10.1007/s11831-020-09409-1

Kumar, A., Kumar, M., & Kaur, A. (2021). Face detection in still images under occlusion and non-uniform illumination. Multimedia Tools and Applications, 80(10), 14565–14590.

https://link.springer.com/article/10.1007/s11042-020-10457-9

Kaur, R. P., Jindal, M. K., & Kumar, M. (2021). Text and graphics segmentation of newspapers printed in Gurmukhi script: a hybrid approach. The Visual Computer, 37(7), 1637–1659.

https://link.springer.com/article/10.1007/s00371-020-01927-0

Bansal, M., Kumar, M., & Kumar, M. (2021). 2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multimedia Tools and Applications, 80(12), 18839–18857.

https://link.springer.com/article/10.1007/s11042-021-10646-0

Singh, S., Ahuja, U., Kumar, M., Kumar, K., & Sachdeva, M. (2021). Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimedia Tools and Applications, 80(13), 19753–19768.

https://doi.org/10.1007/s11042-021-10711-8

Ghosh, K. K., Begum, S., Sardar, A., Adhikary, S., Ghosh, M., Kumar, M., & Sarkar, R. (2021). Theoretical and empirical analysis of filter ranking methods. Expert Systems with Applications, 169, 114485.

https://doi.org/10.1016/j.eswa.2020.114485

Gupta, S., Thakur, K., & Kumar, M. (2021). 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions. The Visual Computer, 37(3), 447–456.

https://link.springer.com/article/10.1007/s00371-020-01814-8

Walia, S., Kumar, K., Kumar, M., & Gao, X. Z. (2021). Fusion of handcrafted and deep features for forgery detection in digital images. IEEE Access, 9, 99742–99755.

https://ieeexplore.ieee.org/abstract/document/9481119

Mushtaq, F., Misgar, M. M., Kumar, M., & Khurana, S. S. (2021). UrduDeepNet: offline handwritten Urdu character recognition using deep neural network. Neural Computing and Applications, 33(22), 15229–15252.

https://link.springer.com/article/10.1007/s00521-021-06144-x

Kumar, M., Jindal, M. K., & Kumar, M. (2021). A novel attack on monochrome and greyscale Devanagari CAPTCHAs. Transactions on Asian and Low-Resource Language Information Processing, 20(4), 1–30.

https://dl.acm.org/doi/10.1145/3439798

Kumar, M., Singh, N., Kumar, R., Goel, S., & Kumar, K. (2021). Gait recognition based on vision systems: A systematic survey. Journal of Visual Communication and Image Representation, 75, 103052.

https://doi.org/10.1016/j.jvcir.2021.103052

Gupta, S., Mohan, N., & Kumar, M. (2021). A study on source device attribution using still images. Archives of Computational Methods in Engineering, 28(4), 2209–2223.

https://link.springer.com/article/10.1007/s11831-020-09452-y

Kapoor, K., Rani, S., Kumar, M., Chopra, V., & Brar, G. S. (2021). Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition. Multimedia Tools and Applications, 80(10), 15233–15271.

https://link.springer.com/article/10.1007/s11042-021-10548-1

Shaheed, K., Mao, A., Qureshi, I., Kumar, M., Abbas, Q., Ullah, I., & Zhang, X. (2021). A systematic review on physiological-based biometric recognition systems: current and future trends. Archives of Computational Methods in Engineering, 28(7), 4917–4960.

https://link.springer.com/article/10.1007/s11831-021-09560-3

Ayyagari, M. R., Kesswani, N., Kumar, M., & Kumar, K. (2021). Intrusion detection techniques in network environment: a systematic review. Wireless Networks, 27(2), 1269–1285.

https://link.springer.com/article/10.1007/s11276-020-02529-3

Goyal, L. M., Mittal, M., Kumar, M., Kaur, B., Sharma, M., Verma, A., & Kaur, I. (2021). An efficient method of multicolor detection using global optimum thresholding for image analysis. Multimedia Tools and Applications, 80(12), 18969–18991.

https://link.springer.com/article/10.1007/s11042-020-10365-y

Kaur, H., & Kumar, M. (2021). Offline handwritten Gurumukhi word recognition using eXtreme Gradient Boosting methodology. Soft Computing, 25(6), 4451–4464.

https://doi.org/10.1007/s00500-020-05455-w

Bansal, M., Kumar, M., Kumar, M., & Kumar, K. (2021). An efficient technique for object recognition using Shi-Tomasi corner detection algorithm. Soft Computing, 25(6), 4423–4432.

https://doi.org/10.1007/s00500-020-05453-y

Kaur, H., & Kumar, M. (2021). On the recognition of offline handwritten word using holistic approach and AdaBoost methodology. Multimedia Tools and Applications, 80(7), 11155–11175.

https://link.springer.com/article/10.1007/s11042-020-10297-7

Gupta, S., & Kumar, M. (2020). Forensic document examination system using boosting and bagging methodologies. Soft Computing, 24(7), 5409–5426.

https://link.springer.com/article/10.1007/s00500-019-04297-5

Narang, S. R., Jindal, M. K., & Kumar, M. (2020). Line segmentation of Devanagari ancient manuscripts. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 90(4), 717–724.

https://link.springer.com/article/10.1007/s40010-019-00627-2

Kaur, R. P., Kumar, M., & Jindal, M. K. (2020). Newspaper text recognition of Gurumukhi script using random forest classifier. Multimedia Tools and Applications, 79(11), 7435–7448.

https://link.springer.com/article/10.1007/s11042-019-08365-8

Kumar, M., Jindal, M. K., Sharma, R. K., & Jindal, S. R. (2020). Performance evaluation of classifiers for the recognition of offline handwritten Gurumukhi characters and numerals: a study. Artificial Intelligence Review, 53(3), 2075–2097.

https://link.springer.com/article/10.1007/s10462-019-09727-2

Kumar, M., Gupta, S., Kumar, K., & Sachdeva, M. (2020). Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model. Digital Government: Research and Practice, 1(4), 1–9.

https://doi.org/10.1145/3411760

Parray, I. R., Khurana, S. S., Kumar, M., & Altalbe, A. A. (2020). Time series data analysis of stock price movement using machine learning techniques. Soft Computing, 24(21).

https://link.springer.com/article/10.1007/s00500-020-04957-x

Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114.

https://doi.org/10.1016/j.eswa.2019.113114

Dargan, S., Kumar, M., Garg, A., & Thakur, K. (2020). Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM. Soft Computing, 24(13).

https://doi.org/10.1007/s00500-019-04525-y

Narang, S. R., Jindal, M. K., Ahuja, S., & Kumar, M. (2020). On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features. Soft Computing, 24(22), 17279–17289.

https://link.springer.com/article/10.1007/s00500-020-05018-z

Kumar, M., Gupta, S., & Mohan, N. (2020). A computational approach for printed document forensics using SURF and ORB features. Soft Computing, 24(17).

https://link.springer.com/article/10.1007/s00500-020-04733-x

Singh, A., Kadyan, V., Kumar, M., & Bassan, N. (2020). ASRoIL: a comprehensive survey for automatic speech recognition of Indian languages. Artificial Intelligence Review, 53(5), 3673–3704.

https://link.springer.com/article/10.1007/s10462-019-09775-8

Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2020). A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4), 1071–1092.

https://link.springer.com/article/10.1007/s11831-019-09344-w

Narang, S. R., Jindal, M. K., & Kumar, M. (2020). Ancient text recognition: a review. Artificial Intelligence Review, 53(8), 5517–5558.

https://link.springer.com/article/10.1007/s10462-020-09827-4

Chhabra, P., Garg, N. K., & Kumar, M. (2020). Content-based image retrieval system using ORB and SIFT features. Neural Computing and Applications, 32(7), 2725–2733.

https://link.springer.com/article/10.1007/s00521-018-3677-9

Kumar, M., Jindal, M. K., Sharma, R. K., & Jindal, S. R. (2019). Character and numeral recognition for non-Indic and Indic scripts: a survey. Artificial Intelligence Review, 52(4), 2235–2261.

https://link.springer.com/article/10.1007/s10462-017-9607-x

Narang, S. R., Jindal, M. K., & Kumar, M. (2019). Devanagari ancient character recognition using DCT features with adaptive boosting and bootstrap aggregating. Soft Computing, 23(24), 13603–13614.

https://doi.org/10.1007/s00500-019-03897-5

Narang, S., Jindal, M. K., & Kumar, M. (2019). Devanagari ancient documents recognition using statistical feature extraction techniques. Sādhanā, 44(6), 141.

https://link.springer.com/article/10.1007/s12046-019-1126-9

Narang, S. R., Jindal, M. K., & Kumar, M. (2019). Drop flow method: an iterative algorithm for complete segmentation of Devanagari ancient manuscripts. Multimedia Tools and Applications, 78(16), 23255–23280.

https://link.springer.com/article/10.1007/s11042-019-7620-6

Kumar, A., Kaur, A., & Kumar, M. (2019). Face detection techniques: a review. Artificial Intelligence Review, 52(2), 927–948.

https://link.springer.com/article/10.1007/s10462-018-9650-2

Kumar, M., & Jindal, S. R. (2019). Fusion of RGB and HSV colour space for foggy image quality enhancement. Multimedia Tools and Applications, 78(8), 9791–9799.

https://link.springer.com/article/10.1007/s11042-018-6599-8

Gupta, S., Kumar, M., & Garg, A. (2019). Improved object recognition results using SIFT and ORB feature detector. Multimedia Tools and Applications, 78(23), 34157–34171.

https://link.springer.com/article/10.1007/s11042-019-08232-6

Kumar, M., Jindal, S. R., Jindal, M. K., & Lehal, G. S. (2019). Improved recognition results of medieval handwritten Gurmukhi manuscripts using boosting and bagging methodologies. Neural Processing Letters, 50(1), 43–56.

https://link.springer.com/article/10.1007/s11063-018-9913-6

Kumar, M., & Jindal, S. R. (2019). A study on recognition of pre-segmented handwritten multi-lingual characters. Archives of Computational Methods in Engineering, 1–13.

https://doi.org/10.1007/s11831-019-09332-0

Kumar, M., Gupta, S., Gao, X. Z., & Singh, A. (2019). Plant species recognition using morphological features and adaptive boosting methodology. IEEE Access, 7, 163912–163918.

https://ieeexplore.ieee.org/abstract/document/8894140

Kaur, P., Kumar, R., & Kumar, M. (2019). A healthcare monitoring system using random forest and internet of things (IoT). Multimedia Tools and Applications, 78(14), 19905–19916.

https://link.springer.com/article/10.1007/s11042-019-7327-8

Kaur, R. P., Jindal, M. K., & Kumar, M. (2019). Recognition of newspaper printed in Gurumukhi script. Journal of Central South University, 26(9), 2495–2503.

https://link.springer.com/article/10.1007/s11771-019-4189-1

Goel, S., Kumar, R., Kumar, M., & Chopra, V. (2019). An efficient page ranking approach based on vector norms using sNorm (p) algorithm. Information Processing & Management, 56(3), 1053–1066.

https://doi.org/10.1016/j.ipm.2019.02.004

Dargan, S., & Kumar, M. (2019). Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey. Archives of Computational Methods in Engineering, 26(4), 1283–1311.

https://link.springer.com/article/10.1007/s11831-018-9278-z

Kaur, H., & Kumar, M. (2018). A comprehensive survey on word recognition for non-Indic and Indic scripts. Pattern Analysis and Applications, 21(4), 897–929.

https://link.springer.com/article/10.1007/s10044-018-0731-2

Kumar, M., Jindal, M. K., Sharma, R. K., & Jindal, S. R. (2018). A novel framework for writer identification based on pre-segmented Gurmukhi characters. Sādhanā, 43(12), 197.

https://link.springer.com/article/10.1007/s12046-018-0966-z

Kumar, M., Jindal, M. K., & Sharma, R. K. (2018). A novel handwriting grading system using gurmukhi characters. International Arab Journal of Information Technology, 15(6), 945–950.

Kumar, M., Chhabra, P., & Garg, N. K. (2018). An efficient content based image retrieval system using BayesNet and K-NN. Multimedia Tools and Applications, 77(16), 21557–21570.

https://link.springer.com/article/10.1007/s11042-017-5587-8

Arora, M., Kumar, M., & Garg, N. K. (2018). Facial emotion recognition system based on PCA and gradient features. National Academy Science Letters, 41(6), 365–368.

https://link.springer.com/article/10.1007/s40009-018-0694-2

Kumar, M., Jindal, M. K., Sharma, R. K., & Jindal, S. R. (2018). Offline handwritten numeral recognition using combination of different feature extraction techniques. National Academy Science Letters, 41(1), 29–33.

https://link.springer.com/article/10.1007/s40009-017-0606-x

Garg, D., Garg, N. K., & Kumar, M. (2018). Underwater image enhancement using blending of CLAHE and percentile methodologies. Multimedia Tools and Applications, 77(20), 26545–26561.

https://link.springer.com/article/10.1007/s11042-018-5878-8

Kaur, H., & Kumar, M. (2018). Benchmark dataset: offline handwritten gurmukhi city names for postal automation. Workshop on Document Analysis and Recognition, 152–159.

https://link.springer.com/chapter/10.1007/978-981-13-9361-7_14

Kumar, M., Jindal, M. K., Sharma, R. K., & Rani Jindal, S. (2018). Performance comparison of several feature selection techniques for offline handwritten character recognition. IEEE RICE.

https://ieeexplore.ieee.org/abstract/document/8509076

Khatri, M., Kumar, M., & Jain, A. (2018). Pulmonary Lesion Detection and Staging from CT Images Using Watershed Algorithm. IEEE IACC.

https://ieeexplore.ieee.org/abstract/document/8692125

Kaur, R. P., Jindal, M. K., & Kumar, M. (2018). Zone segmentation of a text line printed in Gurmukhi script newspaper. IEEE PDGC.

https://ieeexplore.ieee.org/abstract/document/8745796

Kumar, M., Jindal, M. K., & Sharma, R. K. (2017). A novel technique for line segmentation in offline handwritten Gurmukhi script documents. National Academy Science Letters, 40(4), 273–277.

https://link.springer.com/article/10.1007/s40009-017-0558-1

Kaur, R., Kumar, M., & Garg, N. K. (2017). Automated plant leaf recognition using metrics based approach. World Wide Journal of Multidisciplinary Research and Development, 3(7), 162–165.

Kumar, M., & Jindal, S. R. (2017). Devanagari handwriting grading system based on curvature features. Computer Modeling in Engineering & Sciences, 113(2), 195–202.

https://www.ingentaconnect.com/contentone/tsp/cmes/2017/00000113/00000002/art00005

Kumar, M., Jindal, M. K., & Sharma, R. K. (2017). Offline handwritten Gurmukhi character recognition: analytical study of different transformations. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87(1), 137–143.

https://link.springer.com/article/10.1007/s40010-016-0284-y

Koundal, K., Kumar, M., & Garg, N. K. (2017). Punjabi optical character recognition: a survey. Indian Journal of Science and Technology, 10(19), 1–8.

Kumar, M., Jindal, M. K., & Sharma, R. K. (2016). A novel framework for grading of writers using offline Gurmukhi characters. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 86(3), 405–415.

https://link.springer.com/article/10.1007/s40010-016-0277-x

Kumar, M., Jindal, M. K., & Sharma, R. K. (2016). A novel framework for grading of writers using offline Gurmukhi characters. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 86(3), 405–415.

https://link.springer.com/article/10.1007/s40010-016-0277-x

Kumar, M., Jindal, M., Jindal, S., & Sharma, R. (2016). Offline handwritten pre-segmented character recognition of Gurmukhi script. Machine Graphics & Vision, 25(1/4), 45–55.

https://doi.org/10.22630/MGV.2016.25.1.5

Garg, N., & Kumar, M. (2015). Clustering of multi scripts isolated characters using k-means algorithm. International Journal of Mathematical Sciences and Computing, 22–29.

Kumar, M., Sharma, R. K., & Jindal, M. K. (2014). Efficient feature extraction techniques for offline handwritten Gurmukhi character recognition. National Academy Science Letters, 37(4), 381–391.

https://link.springer.com/article/10.1007/s40009-014-0253-4

Kumar, M., Jindal, M. K., & Sharma, R. K. (2014). Segmentation of isolated and touching characters in offline handwritten Gurmukhi script recognition. International Journal of Information Technology and Computer Science, 6(2), 58–63.

Bansal, S., Garg, M., & Kumar, M. (2014). A technique for offline handwritten character recognition. IJCAT International Journal of Computing and Technology, 1(2), 2415–1521.

Kumar, M., Sharma, R. K., & Jindal, M. K. (2013). A novel feature extraction technique for offline handwritten Gurmukhi character recognition. IETE Journal of Research, 59(6), 687–691.

Kumar, M., Jindal, M. K., & Sharma, R. K. (2013). MDP feature extraction technique for offline handwritten Gurmukhi character recognition. SmartCR, 3(6), 397–404.

Kumar, M., Sharma, R. K., & Jindal, M. K. (2013). Size of training set vis-a-vis recognition accuracy of handwritten character recognition system. Journal of Emerging Technologies in Web Intelligence, 5(4), 380–385.

Ahuja, D., & Kumar, M. (2012). Handwritten Numerals Recognition using Hough Transformation Technique. International Journal of Advanced Research in Computer Science, 3(5).

Kumar, M., Jindal, M. K., & Sharma, R. K. (2012). Offline handwritten Gurmukhi character recognition: study of different feature-classifier combinations. Proceedings of the Workshop on Document Analysis and Recognition, 94–99.

https://dl.acm.org/doi/abs/10.1145/2432553.2432571

Kumar, M., Jindal, M. K., & Sharma, R. K. (2011, November). Classification of characters and grading writers in offline handwritten Gurmukhi script. In 2011 International Conference on Image Information Processing (pp. 1–4). IEEE.

https://ieeexplore.ieee.org/abstract/document/6108859

Kumar, M., Jindal, M. K., & Sharma, R. K. (2011, September). Review on OCR for handwritten Indian scripts character recognition. In International Conference on Digital Image Processing and Information Technology (pp. 268–276). Springer.

https://link.springer.com/chapter/10.1007/978-3-642-24055-3_28

Kumar, M., Jindal, M. K., & Sharma, R. K. (2011, November). k-nearest neighbor based offline handwritten Gurmukhi character recognition. In 2011 International Conference on Image Information Processing (pp. 1–4). IEEE.

https://ieeexplore.ieee.org/abstract/document/6108863

Kumar, M. (2010). Comparing the Effectiveness of PAGERANK, HITS and sNorm (p) Web Page Ranking Algorithms. International Journal of Computer Applications, 975, 8887.

Kumar, M., Sharma, R. K., & Jindal, M. K. (2010). Review on OCR for documents in handwritten Indian scripts. In National Conference on Recent Advances in Computational Techniques in Electrical Engineering (pp. 1–6).

Kumar, M., Sharma, R. K., & Jindal, M. K. (2010, December). Segmentation of lines and words in handwritten Gurmukhi script documents. In Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia (pp. 25–28).

https://dl.acm.org/doi/abs/10.1145/1963564.1963568

Kumar, M. (2010). A New Approach for Web Page Ranking Solution: sNorm (p) Algorithm. International Journal of Computer Applications, 9(10), 20–23.